Omics Data Analysis

Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015), ) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).

Inferential Statistics

Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Xiao (Xiao (2018), ), Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), ), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), ) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), ; Shieh (2018), ), mean difference plot (Boehning et al. (2008), ), plot of test statistic for permutation and bootstrap tests as well as objects of class htest.

Tools for Statistical Disclosure Control in Research Data Centers

Tools for researchers to explicitly show that their results comply to rules for statistical disclosure control imposed by research data centers. These tools help in checking descriptive statistics and models and in calculating extreme values that are not individual data. Also included is a simple function to create log files. The methods used here are described in the "Guidelines for the checking of output based on microdata research" by Bond, Brandt, and de Wolf (2015) < https://cros.ec.europa.eu/system/files/2024-02/Output-checking-guidelines.pdf>.

Scalable Gaussian-Process Approximations

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) . Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) and MaxMin ordering proposed in Guinness (2018) .

Article Formats for R Markdown

A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.

Permutational Group Sequential Test for Time-to-Event Data

Permutational group-sequential tests for time-to-event data based on the log-rank test statistic. Supports exact permutation test when the censoring distributions are equal in the treatment and the control group and approximate imputation-permutation methods when the censoring distributions are different.

High Dimensional Categorical Data Visualization

Easy visualization for datasets with more than two categorical variables and additional continuous variables. The package is particularly useful for exploring complex categorical data in the context of pathway analysis across multiple conditions. This package is now in maintenance-only mode and kept for legacy compatibility; for new projects and active development, please use the successor package 'ggdiceplot' (see < https://github.com/maflot/ggdiceplot> and < https://dice-and-domino-plot.readthedocs.io/en/latest/>).

A Time Series Database for Official Statistics with R and PostgreSQL

Archive and manage times series data from official statistics. The 'timeseriesdb' package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information.

Simulation and Data Analysis for Plant Breeders

Provides tools for simulation of plant breeding programs as described, for example, by Melchinger and Frisch (2023) , prediction of segregation variance (Osthushenrich, Frisch and Herzog (2017) ), genomic prediction (Hofheinz and Frisch (2014) ), linkage disequilibrium based haplotype construction, and planning of marker assisted back crossing programs. It provides an integrated framework for simulation and analysis of plant breeding programs.

Parametric Time-to-Event Analysis with Variable Incubation Phases

Fit parametric models for time-to-event data that show an initial 'incubation period', i.e. a variable delay phase where no events occur. The delayed Weibull distribution serves as the foundational data model. For parameter estimation, different flavours of maximum likelihood estimation ('MLE') and the method of maximum product of spacings estimation ('MPSE') are implemented. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.